Methods from the above three groups can be used to perform unlearning for classification models, as long as the relevant training data (e.g., \(\mathbf {D}\)  and \(\mathbf {D_i}\) ) are possible to globally access at hand.
However, in the FL settings, since the number of participant devices \(\mathbf {n}\)  is usually much smaller than the total devices \(\mathbf {m}\)  [1]}, and the data on the participant devices is usually Non-IID [2]}, thereby leading to the participant data being incomplete and severely biased.
This practical limitation makes inaccurate approximation for the federated unlearning, otherwise to obtain accurate approximation requiring a similar participant cost than the full retrain.
